LangGraph vs LlamaIndex vs Haystack: Picking the Orchestration Layer of Your Agent Stack
The question langgraph vs llamaindex gets asked constantly, and it is slightly the wrong question, which is exactly why it deserves a proper answer. These two, plus Haystack, are the three frameworks builders most often shortlist for the orchestration layer of an agent stack, and they did not start from the same place. LangGraph grew out of control flow. LlamaIndex grew out of data. Haystack grew out of search pipelines that predate the agent era entirely. By 2026 all three ship agent abstractions, all three claim production readiness, and all three will happily run the same demo in an afternoon.
The differences show up later, in the parts of the system you cannot screenshot: how state is persisted, how a pipeline fails, which commercial platform the framework quietly pulls you toward, and what your team can still debug at 2am six months in. We wire these things together for real workloads and keep notes. This is the cleaned-up version of those notes: what each framework actually is, how they compose, and how to choose in five questions.
What each one actually is
LangGraph is the agent runtime from the LangChain team, best understood as a state machine library for LLM applications. You model an agent as a graph: nodes do work, edges route between them, and state is an explicit object that persists across steps. Checkpointing, human-in-the-loop interrupts, retries and replay-style debugging all fall out of that one design decision. Most 2026 comparisons treat it as the default pick for complex, stateful agent control flow, and that matches what we see in production codebases: when the workflow has branches, cycles and approval gates, LangGraph is usually the framework holding it.
LlamaIndex approaches from the data side. Its core competency is ingestion, indexing and retrieval: connectors, chunking, embeddings, query engines, plus commercial services for parsing ugly documents at scale. Its orchestration story is event-driven Workflows, where typed steps react to events rather than sit in a graph. The result reads like clear procedural code and debugs like it too. It is the framework you pick when the hard part of your agent is the knowledge, not the choreography.
Haystack, from deepset, was running production NLP pipelines before agents were a category. The 2.x rewrite made everything a typed component wired into an explicit, serializable pipeline, and agent capabilities arrived as an evolution of that model rather than a bolt-on. It is the least hyped of the three and the one we meet most often in regulated environments, where its pipeline discipline and built-in evaluation story are not nice-to-haves but requirements.
The comparison, builder's cut
| LangGraph | LlamaIndex | Haystack | |
|---|---|---|---|
| Center of gravity | Control flow and state | Data ingestion and retrieval | Typed, explicit pipelines |
| Agent model | Graph with persistent state | Event-driven workflows | Components plus agent loops |
| Where it shines | Long-running, stateful, human-in-the-loop agents | RAG-heavy agents over messy corpora | Auditable production pipelines |
| Weak spot | Abstraction stack to learn, ecosystem sprawl | Thinner story for complex control flow | Smaller agent ecosystem |
| Commercial pull | LangSmith observability, platform deployment | Managed parsing and indexing services | deepset enterprise platform |
| Pick it when | The workflow is the product | The corpus is the product | The pipeline must be defensible |
Composition notes: they stack more than they compete
The cleanest pattern we see in 2026 production systems uses two of these at once. LlamaIndex owns the data plane: ingestion, chunking, one query engine per corpus. LangGraph owns the control plane: each query engine gets wrapped as a tool and called from graph nodes, while LangGraph holds conversation state, retries and escalation paths. Both projects document this integration, and it beats forcing either framework to do the other's job. Haystack composes differently. It prefers to own a pipeline end to end, which is a strength when you want one inspectable artifact and a limitation when you want to mix vendors. Running Haystack retrieval under a LangGraph controller works, but you are maintaining two mental models, and most teams that start there consolidate within a couple of quarters. One thing that does not compose well anywhere: two orchestrators. Pick a single place where control flow lives. Agents calling agents across frameworks is where debugging goes to die.
Lock-in and graveyard risk
None of the three is a graveyard candidate right now. All are actively maintained with real commercial backing, which is more than most of the agent ecosystem can say. The lock-in risks are subtler. LangGraph's gravity pulls toward LangSmith for observability and the team's managed platform for deployment: excellent tools, real switching costs, budget for them consciously. LlamaIndex's managed parsing and indexing services are genuinely good at ugly documents and meter by usage, so data-heavy teams should model that bill before it models them. Haystack's open-source core is unusually self-contained, with the commercial pull living in deepset's enterprise platform rather than inside the framework. Our standing advice is boring and load-bearing: isolate whichever framework you pick behind your own interfaces, keep prompts and evals in your own repo, and treat the framework as a dependency you could replace in a quarter. Teams that did this crossed major version bumps without drama. Teams that did not are still running the versions they started on, and they know it.
Version velocity is part of the architecture
All three ship fast enough that your lockfile is a design document. Interfaces that look settled get deprecated, integration packages split and merge, and a minor release can rearrange imports. This is not a complaint, it is the cost of building on a layer that is still being invented. Practically: pin versions, read changelogs before upgrading rather than after, keep one scheduled upgrade window a month, and run your own eval suite as the regression gate. The framework's own tests do not know what your agents are supposed to do. Yours should.
How to choose in five questions
- Is your hard problem choreography or knowledge? Choreography points to LangGraph, knowledge points to LlamaIndex.
- Do you need durable state, pauses and human approval steps in the loop? That is LangGraph's home turf, natively.
- Does most of your value sit in messy documents at ingestion time? LlamaIndex, and budget honestly for managed parsing.
- Will auditors, security reviews or regulators read your architecture diagrams? Haystack's explicit pipelines are the easiest to defend in that room.
- Still tied? Prototype the same thin slice in two frameworks for one week. The one your team debugs faster wins, and that result outranks any comparison post, including this one.
Frequently asked questions
Is LangGraph the same thing as LangChain?
No. LangGraph is the agent runtime from the LangChain team. It models agents as graphs with explicit, persistent state, and you can adopt it with or without classic LangChain components in the same codebase.
Can I use LlamaIndex and LangGraph together?
Yes, and it is a common production pattern: LlamaIndex owns ingestion, indexing and retrieval while LangGraph owns control flow. Wrap the query engine as a tool and call it from a graph node.
Is Haystack only for RAG search?
Not anymore. Haystack predates the agent era as a search pipeline framework, and its 2.x component model now covers agents, tool calling and evaluation. It remains strongest where pipeline discipline matters.
Which one should a small team default to in 2026?
If you must pick blind: LangGraph when agent behavior is the product, LlamaIndex when the product is mostly retrieval over documents, Haystack when audits loom. Then isolate it behind your own interfaces so the choice stays reversible.
This corner of the map redraws itself faster than any other layer we track: new runtimes appear, integration packages get abandoned, and last quarter's default becomes this quarter's graveyard entry. We publish the running version of this comparison, plus two composition notes and one graveyard entry each issue, in the Monthly Stack Report. If you are wiring an agent stack this year, that is the cheapest insurance we know of.